Modifying search result ranking based on implicit user feedback

ABSTRACT

The present disclosure includes systems and techniques relating to ranking search results of a search query. In general, the subject matter described in this specification can be embodied in a computer-implemented method that includes determining a measure of relevance for a document result within a context of a search query for which the document result is returned, the determining being based on a first number in relation to a second number, the first number corresponding to longer views of the document result, and the second number corresponding to at least shorter views of the document result; and outputting the measure of relevance to a ranking engine for ranking of search results, including the document result, for a new search corresponding to the search query. The subject matter described in this specification can also be embodied in various corresponding computer program products, apparatus and systems.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.11/556,143, filed Nov. 2, 2006, the entirety of which is incorporated byreference herein.

BACKGROUND

The present disclosure relates to ranking of search results.

Internet search engines aim to identify documents or other items thatare relevant to a user's needs and to present the documents or items ina manner that is most useful to the user. Such activity often involves afair amount of mind-reading—inferring from various clues what the userwants. Certain clues may be user specific. For example, knowledge that auser is making a request from a mobile device, and knowledge of thelocation of the device, can result in much better search results forsuch a user.

Clues about a user's needs may also be more general. For example, searchresults can have an elevated importance, or inferred relevance, if anumber of other search results link to them. If the linking results arethemselves highly relevant, then the linked-to results may have aparticularly high relevance. Such an approach to determining relevance,generally associated with the GOOGLE® PageRank technology, is premisedon the assumption that, if authors of web pages felt that another website was relevant enough to be linked to, then web searchers would alsofind the site to be particularly relevant. In short, the web authors“vote up” the relevance of the sites.

Other various inputs may be used instead of, or in addition to, suchtechniques for determining and ranking search results. For example, userreactions to particular search results or search result lists may begauged, so that results on which users often click will receive a higherranking. The general assumption under such an approach is that searchingusers are often the best judges of relevance, so that if they select aparticular search result, it is likely to be relevant, or at least morerelevant than the presented alternatives.

SUMMARY

Systems, methods, and apparatus including computer program products forranking search results of a search query are described. In general,particular inputs may be generated or analyzed to affect thepresentation of search results. For example, such inputs may increasethe relevance that a system will assign to a particular result in aparticular situation, thus boosting the score or other indicator ofrelevance for the result (and perhaps the relevance of the result in thecontext of a particular query). Such an approach may benefit a user byproviding them with search results that are more likely to match theirneeds. As a result, users can learn more using the internet, can findmore relevant information more quickly, and will thus achieve more intheir work or elsewhere, and will be more likely to use such a systemagain. A provider of such services may also benefit, by providing moreuseful services to users, and by thereby inducing more traffic to theirsearch services. Such additional traffic may provide an operator withadditional revenue, such as in the form of advertising that accompaniesthe searching and the delivery of search results.

One aspect of the subject matter described in this specification can beembodied in a computer-implemented method that includes determining ameasure of relevance for a document result within a context of a searchquery for which the document result is returned, the determining beingbased on a first number in relation to a second number, the first numbercorresponding to longer views of the document result, and the secondnumber corresponding to at least shorter views of the document result;and outputting the measure of relevance to a ranking engine for rankingof search results, including the document result, for a new searchcorresponding to the search query. The first number can include a numberof the longer views of the document result, the second number caninclude a total number of views of the document result, and thedetermining can include dividing the number of longer views by the totalnumber of views.

The method can further include tracking individual selections of thedocument result within the context of the search query for which thedocument result is returned; weighting document views resulting from theselections based on viewing length information to produce weighted viewsof the document result; and combining the weighted views of the documentresult to determine the first number. The second number can include atotal number of views of the document result, the determining caninclude dividing the first number by the second number, and the measureof relevance can be independent of relevance for other document resultsreturned in response to the search query.

The weighting can include applying a continuous function to the documentviews resulting from the selections. The weighting can include applyinga discontinuous function to the document views resulting from theselections. Applying the discontinuous function can include classifyingthe individual selections of the document result into viewing timecategories; and assigning weights to the individual selections based onresults of the classifying.

The weighting can include weighting the document views based on theviewing length information in conjunction with a viewing lengthdifferentiator. The viewing length differentiator can include a factorgoverned by a determined category of the search query, and the weightingcan include weighting the document views based on the determinedcategory of the search query. The viewing length differentiator caninclude a factor governed by a determined type of a user generating theindividual selections, and the weighting can include weighting thedocument views based on the determined type of the user.

The subject matter described in this specification can also be embodiedin various corresponding computer program products, apparatus andsystems. For example, a system can include a tracking component and arank modifier engine structured to perform the operations described.Moreover, a system can include various means for performing theoperations described, as detailed below, and equivalents thereof.

Particular embodiments of the described subject matter can beimplemented to realize one or more of the following advantages. Aranking sub-system can include a rank modifier engine that uses implicituser feedback to cause re-ranking of search results in order to improvethe final ranking presented to a user of an information retrievalsystem. User selections of search results (click data) can be trackedand transformed into a click fraction that can be used to re-rank futuresearch results. Data can be collected on a per-query basis, and for agiven query, user preferences for document results can be determined.Moreover, a measure of relevance (e.g., an LC|C click fraction) can bedetermined from implicit user feedback, where the measure of relevancecan be independent of relevance for other document results returned inresponse to the search query, and the measure of relevance can reducethe effects of presentation bias (in the search results shown to auser), which might otherwise bleed into the implicit feedback.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,aspects, and advantages will become apparent from the description, thedrawings, and the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example information retrieval system in which therelevance of results obtained for submitted search queries can beimproved.

FIG. 2 shows example components of an information retrieval system.

FIG. 3 shows another example information retrieval system.

FIG. 4A shows an example process of generating a measure of relevancefor a document for use in improving a search results' ranking.

FIGS. 4B and 4C show example weighting functions.

FIG. 4D shows an example process of discontinuous weighting.

FIG. 4E shows an example process of weighting based on one or moreviewing length differentiators.

FIG. 5 is a schematic diagram of an example computer system.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 shows an example system 1000 for improving the relevance ofresults obtained from submitting search queries as can be implemented inan internet, intranet, or other client/server environment. The system1000 is an example of an information retrieval system in which thesystems, components and techniques described below can be implemented.Although several components are illustrated, there may be fewer or morecomponents in the system 1000. Moreover, the components can bedistributed on one or more computing devices connected by one or morenetworks or other suitable communication mediums.

A user 1002 (1002 a, 1002 b, 1002 c) can interact with the system 1000through a client device 1004 (1004 a, 1004 b, 1004 c) or other device.For example, the client device 1004 can be a computer terminal within alocal area network (LAN) or wide area network (WAN). The client device1004 can include a random access memory (RAM) 1006 (or other memoryand/or a storage device) and a processor 1008. The processor 1008 isstructured to process instructions within the system 1000. In someimplementations, the processor 1008 is a single-threaded processor. Inother implementations, the processor 1008 is a multi-threaded processor.The processor 1008 can include multiple processing cores and isstructured to process instructions stored in the RAM 1006 (or othermemory and/or a storage device included with the client device 1004) todisplay graphical information for a user interface.

A user 1002 a can connect to the search engine 1030 within a serversystem 1014 to submit a query 1015. When the user 1002 a submits thequery 1015 through an input device attached to a client device 1004 a, aclient-side query signal 1010 a is sent into a network 1012 and isforwarded to the server system 1014 as a server-side query signal 1010b. Server system 1014 can be one or more server devices in one or morelocations. A server device 1014 includes a memory device 1016, which caninclude the search engine 1030 loaded therein. A processor 1018 isstructured to process instructions within the device 1014. Theseinstructions can implement one or more components of the search engine1030. The processor 1018 can be a single-threaded processor or amulti-threaded processor, and can include multiple processing cores. Theprocessor 1018 can process instructions stored in the memory 1016related to the search engine 1030 and can send information to the clientdevice 1004, through the network 1012, to create a graphicalpresentation in a user interface of the client device 1004 (e.g., asearch results web page displayed in a web browser).

The server-side query signal 1010 b is received by the search engine1030. The search engine 1030 uses the information within the user query1015 (e.g. query terms) to find relevant documents. The search engine1030 can include an indexing engine 1020 that actively searches a corpus(e.g., web pages on the Internet) to index the documents found in thatcorpus, and the index information for the documents in the corpus can bestored in an index database 1022. This index database 1022 can beaccessed to identify documents related to the user query 1015. Notethat, an electronic document (which for brevity will simply be referredto as a document) does not necessarily correspond to a file. A documentcan be stored in a portion of a file that holds other documents, in asingle file dedicated to the document in question, or in multiplecoordinated files.

The search engine 1030 can include a ranking engine 1052 to rank thedocuments related to the user query 1015. The ranking of the documentscan be performed using traditional techniques for determining aninformation retrieval (IR) score for indexed documents in view of agiven query. The relevance of a particular document with respect to aparticular search term or to other provided information may bedetermined by any appropriate technique. For example, the general levelof back-links to a document that contains matches for a search term maybe used to infer a document's relevance. In particular, if a document islinked to (e.g., is the target of a hyperlink) by many other relevantdocuments (e.g., documents that also contain matches for the searchterms), it can be inferred that the target document is particularlyrelevant. This inference can be made because the authors of the pointingdocuments presumably point, for the most part, to other documents thatare relevant to their audience.

If the pointing documents are in turn the targets of links from otherrelevant documents, they can be considered more relevant, and the firstdocument can be considered particularly relevant because it is thetarget of relevant (or even highly relevant) documents. Such a techniquemay be the determinant of a document's relevance or one of multipledeterminants. The technique is exemplified in the GOGGLE® PageRanksystem, which treats a link from one web page to another as anindication of quality for the latter page, so that the page with themost such quality indicators wins. Appropriate techniques can also betaken to identify and eliminate attempts to cast false votes so as toartificially drive up the relevance of a page.

To further improve such traditional document ranking techniques, theranking engine 1052 can receive an additional signal from a rankmodifier engine 1056 to assist in determining an appropriate ranking forthe documents. The rank modifier engine 1056 provides one or moremeasures of relevance for the documents, which can be used by theranking engine 1052 to improve the search results' ranking provided tothe user 1002. The rank modifier engine 1056 can perform one or more ofthe operations described further below to generate the one or moremeasures of relevance.

The search engine 1030 can forward the final, ranked result list withina server-side search results signal 1028 a through the network 1012.Exiting the network 1012, a client-side search results signal 1028 b canbe received by the client device 1004 a where the results can be storedwithin the RAM 1006 and/or used by the processor 1008 to display theresults on an output device for the user 1002 a.

FIG. 2 shows example components of an information retrieval system.These components can include an indexing engine 2010, a scoring engine2020, a ranking engine 2030, and a rank modifier engine 2070. Theindexing engine 2010 can function as described above for the indexingengine 1020. In addition, the scoring engine 2020 can generate scoresfor document results based on many different features, includingcontent-based features that link a query to document results, andquery-independent features that generally indicate the quality ofdocuments results. The content-based features can include aspects ofdocument format, such as query matches to title or anchor text in anHTML (Hyper Text Markup Language) page. The query-independent featurescan include aspects of document cross-referencing, such as the PageRankof the document or the domain. Moreover, the particular functions usedby the scoring engine 2020 can be tuned, to adjust the various featurecontributions to the final IR score, using automatic or semi-automaticprocesses.

The ranking engine 2030 can produce a ranking of document results 2040for display to a user based on IR scores received from the scoringengine 2020 and one or more signals from the rank modifier engine 2070.A tracking component 2050 can be used to record information regardingindividual user selections of the results presented in the ranking 2040.For example, the tracking component 2050 can be embedded JavaScript codeincluded in a web page ranking 2040 that identifies user selections(clicks) of individual document results and also identifies when theuser returns to the results page, thus indicating the amount of time theuser spent viewing the selected document result. In otherimplementations, the tracking component 2050 can be proxy system throughwhich user selections of the document results are routed, or thetracking component can include pre-installed software at the client(e.g., a toolbar plug-in to the client's operating system). Otherimplementations are also possible, such as by using a feature of a webbrowser that allows a tag/directive to be included in a page, whichrequests the browser to connect back to the server with message(s)regarding link(s) clicked by the user.

The recorded information can be stored in result selection logs 2060.The recorded information can include log entries that indicate, for eachuser selection, the query (Q), the document (D), the time (T) on thedocument, the language (L) employed by the user, and the country (C)where the user is likely located (e.g., based on the server used toaccess the IR system). Other information can also be recorded regardinguser interactions with a presented ranking, including negativeinformation, such as the fact that a document result was presented to auser, but was not clicked, position(s) of click(s) in the userinterface, IR scores of clicked results, IR scores of all results shownbefore click, the titles and snippets shown to the user before theclick, the user's cookie, cookie age, IP (Internet Protocol) address,user agent of the browser, etc. Moreover, similar information (e.g., IRscores, position, etc.) can be recorded for an entire session, ormultiple sessions of a user, including potentially recording suchinformation for every click that occurs both before and after a currentclick.

The information stored in the result selection logs 2060 can be used bythe rank modifier engine 2070 in generating the one or more signals tothe ranking engine 2030. In general, a wide range of information can becollected and used to modify or tune the click signal from the user tomake the signal, and the future search results provided, a better fitfor the user's needs. Thus, user interactions with the rankingspresented to the users of the information retrieval system can be usedto improve future rankings.

The components shown in FIG. 2 can be combined in various manners andimplemented in various system configurations. For example, the scoringengine 2020 and the ranking engine 2030 can be merged into a singleranking engine, such as the ranking engine 1052 of FIG. 1. The rankmodifier engine 2070 and the ranking engine 2030 can also be merged, andin general, a ranking engine includes any software component thatgenerates a ranking of document results after a query. Moreover, aranking engine can be included in a client system in addition to (orrather than) in a server system.

FIG. 3 shows another example information retrieval system. In thissystem, a server system 3050 includes an indexing engine 3060 and ascoring/ranking engine 3070. A client system 3000 includes a userinterface for presenting a ranking 3010, a tracking component 3020,result selection logs 3030 and a ranking/rank modifier engine 3040. Forexample, the client system 3000 can include a company's enterprisenetwork and personal computers, in which a browser plug-in incorporatesthe ranking/rank modifier engine 3040. When an employee in the companyinitiates a search on the server system 3050, the scoring/ranking engine3070 can return the search results along with either an initial rankingor the actual IR scores for the results. The browser plug-in can thenre-rank the results locally based on tracked page selections for thecompany-specific user base.

FIG. 4A shows an example process of generating a measure of relevancefor a document for use in improving a search results' ranking.Individual selections of a document result can be tracked 4010 withinthe context of a search query for which the document result is returned.For example, in the context of a web based information retrieval system,user's click data on web page search results can be gathered and storedin logs, which can be kept for all user queries. When a user clicks on asearch result, the click can be tracked via JavaScript code embedded inthe search results page, an embedded browser tag, etc. This code cantrack when and on what a user clicks in the main search results page,and can track when the user returns to that main page.

Post-click behavior can also be tracked via pre-installed software atthe client (e.g., a toolbar plug-in to the client's operating system).Provided the user opts into fully sharing their browsing behavior, thetoolbar software can track all the pages that the user visits, bothbefore and after the search results page is delivered.

The information gathered for each click can include: (1) the query (Q)the user entered, (2) the document result (D) the user clicked on, (3)the time (T) on the document, (4) the interface language (L) (which canbe given by the user), (5) the country (C) of the user (which can beidentified by the host that they use, such as www-google-co-uk toindicate the United Kingdom), and (6) additional aspects of the user andsession. The time (T) can be measured as the time between the initialclick through to the document result until the time the user comes backto the main page and clicks on another document result. In general, anassessment is made about the time (T) regarding whether this timeindicates a longer view of the document result or a shorter view of thedocument result, since longer views are generally indicative of qualityfor the clicked through result. This assessment about the time (T) canfurther be made in conjunction with various weighting techniques.

Document views resulting from the selections can be weighted 4020 basedon viewing length information to produce weighted views of the documentresult. Thus, rather than simply distinguishing long clicks from shortclicks, a wider range of click through viewing times can be included inthe assessment of result quality, where longer viewing times in therange are given more weight than shorter viewing times. This weightingcan be either continuous or discontinuous.

FIGS. 4B and 4C show example weighting functions, plotted against time4110 and weight 4120. A continuous function 4130 can be applied to thedocument views resulting from the selections. Thus, the weight given toa particular click through time can fall within a continuous range ofvalues, as defined by the function 4130. Alternatively, a discontinuousfunction 4140 can be applied to the document views resulting from theselections. As shown in the example of FIG. 4C, there are three viewingtime categories, each having a corresponding weight. Note that thefunction 4140 can be an explicitly defined function, or merely implicitin the software implementation.

FIG. 4D shows an example process of discontinuous weighting. Theindividual selections of the document result can be classified 4210 intoviewing time categories, and weights can be assigned 4220 to theindividual selections based on results of the classifying. For example,a short click can be considered indicative of a poor page and thus givena low weight (e.g., −0.1 per click), a medium click can be consideredindicative of a potentially good page and thus given a slightly higherweight (e.g., 0.5 per click), a long click can be considered indicativeof a good page and thus given a much higher weight (e.g., 1.0 perclick), and a last click (where the user doesn't return to the mainpage) can be considered as likely indicative of a good page and thusgiven a fairly high weight (e.g., 0.9). Note that the click weightingcan also be adjusted based on previous click information. For example,if another click preceded the last click, the last click can beconsidered as less indicative of a good page and given only a moderateweight (e.g., 0.3 per click).

The various time frames used to classify short, medium and long clicks,and the weights to apply, can be determined for a given search engine bycomparing historical data from user selection logs with human generatedexplicit feedback on the quality of search results for various givenqueries, and the weighting process can be tuned accordingly.Furthermore, these time frames and weights can be adjusted based on oneor more viewing length differentiators, as is described further below inconnection with FIG. 4E.

The weighted views of the document result can be combined 4030 todetermine a first number to be used in determining a measure ofrelevance. For example, the weighted clicks described above can besummed together for a given query-document pair. Note that safeguardsagainst spammers (users who generate fraudulent clicks in an attempt toboost certain search results) can be taken to help ensure that the userselection data is meaningful, even when very little data is availablefor a given (rare) query. These safeguards can include employing a usermodel that describes how a user should behave over time, and if a userdoesn't conform to this model, their click data can be disregarded. Thesafeguards can be designed to accomplish two main objectives: (1) ensuredemocracy in the votes (e.g., one single vote per cookie and/or IP for agiven query-URL pair), and (2) entirely remove the information comingfrom cookies or IP addresses that do not look natural in their browsingbehavior (e.g., abnormal distribution of click positions, clickdurations, clicks_per_minute/hour/day, etc.). Suspicious clicks can beremoved, and the click signals for queries that appear to be spammedneed not be used (e.g., queries for which the clicks feature adistribution of user agents, cookie ages, etc. that do not look normal).

A measure of relevance for the document result can be determined 4040within the context of the search query for which the document result isreturned. The determining can be based on a first number in relation toa second number, where the first number corresponds to longer views ofthe document result, and the second number corresponds to at leastshorter views of the document result. The first number can be a numberof the longer views of the document result, or the weighted selections,as described above, where the weights are skewed to longer views of thedocument result.

The second number includes at least the shorter views, such that arelation of long clicks to short clicks can be determined. This measurecan help in reducing the effects of presentation bias, since an amountof time that users spend viewing a given document result is, in a sense,compared with itself. Presentation bias includes various aspects ofpresentation, such as an attractive title or snippet provided with thedocument result, and where the document result appears in the presentedranking (position). Note that users tend to click results with goodsnippets, or that are higher in the ranking, regardless of the realrelevance of the document to the query as compared with the otherresults. By assessing the quality of a given document result for a givenquery, irrespective of the other document results for the given query,this measure of relevance can be relatively immune to presentation bias.

In addition to the shorter views, the second number can also include thelonger views, and need not employ any weighting. For example, the secondnumber can be the total number of views of the document result (anordinal count of clicks on that result). The first number and secondnumber can be combined to form a single feature for use in determiningthe measure of relevance for the document result within the context ofthe search query, and this measure of relevance can be independent ofrelevance for other document results returned in response to the searchquery.

Furthermore, this measure of relevance can be calculated as a fraction,which can be directly applied to IR scores of the search results,thereby boosting the documents in the resulting ranking that haveimplicit user feedback indicating document quality. This fraction isreferred to as the LC|C click fraction since the fraction can begenerally understood as the number of Long Clicks (which may be weightedclicks) divided by the number of Clicks overall for a given documentresult. A detailed description of an LC|C calculation follows, but itwill be understood that variations on the following equations are alsopossible. A base LC|C click fraction can be defined asLCC_BASE=[#WC(Q,D)]/[#C(Q,D)+S0]where #WC(Q,D) is the sum of weighted clicks for a query-URL (UniversalResource Locator) pair, #C(Q,D) is the total number of clicks (ordinalcount, not weighted) for the query-URL pair, and S0 is a smoothingfactor.

The smoothing factor S0 can be chosen such that, if the number ofsamples for the query is low, then the click fraction will tend towardzero. If #C is much larger than S0, then the smoothing factor will notbe a dominant factor. Thus, the smoothing factor can be added as a guardagainst noise in the click fraction.

The LCC_BASE click fraction considers only clicks for a given query-URLpair. At a high level, LC|C can be interpreted as a normalized measureof how long people stayed on a page given that they clicked through tothat page, independent of other results for the query. Thus, the LC|Cclick fraction can be high for a given URL even if it gets far fewerclicks than a comparable result in a higher position.

In addition, the LC|C click fraction can be used on top of a traditionalclick fraction used previously. This traditional click fraction tookinto consideration the other results for the given query, and has beendefined as follows:T_BASE=[#WC(Q,D)]/[#WC(Q)+S0]where #WC(Q,D) is the sum of weighted clicks for a query-URL pair,#WC(Q) is the sum of weighted clicks for the query (summed over allresults for the query), and S0 is a smoothing factor.

LC|C can also employ per-language and per-country fractions (withsmoothing there between) similar to those employed with the traditionalclick fraction:T_LANG=[#WC(Q,D,L)+S1·T_BASE]/[#WC(Q,L)+S1]T_COUNTRY=[#WC(Q,D,L,C)+S2·T_LANG]/[#WC(Q,L,C)+S2]where T_LANG incorporates language specific click data, plus T_BASE, andT_COUNTRY incorporates country (and language) specific click data, plusT_LANG. Thus, the LC|C click fractions can be calculated using:LCC_BASE=[#WC(Q,D)]/[#C(Q,D)+S0]LCC_LANG=[#WC(Q,D,L)+S1·LCC_BASE]/[#C(Q,D,L)+S1]LCC_COUNTRY=[#WC(Q,D,L,C)+S2·LCC_LANG]/[#C(Q,D,L,C)+S2]In this manner, if there is less data for the more specific clickfractions, the overall fraction falls back to the next higher level forwhich more data is available.

Alternatively, the LC|C click fraction can split the smoothing amongclick fractions apart from the smoothing used to determine the thresholdat which the click fraction is considered to begin to provide usefulinformation. Thus, the LC|C click fractions can be defined using a firstset of smoothing parameters:LCC_BASE=[#WC(Q,D)]/[#C(Q,D)+S00]LCC_LANG=[#WC(Q,D,L)]/[#C(Q,D,L)+S10]LCC_COUNTRY=[#WC(Q,D,L,C)]/[#C(Q,D,L,C)+S20]and the final LC|C click fraction can combine these separate clickfractions using a second set of smoothing parameters:LCC_FINAL=X1·LCC_COUNTRY+X2·LCC_LANG+X3·LCC_BASEwhere

X1=[#WC(Q,L,C)]/[#WC(Q,L,C)+S21]

X2=(1−X1)·[#WC(Q,L)]/[#WC(Q,L)+S11]

X3=1−X1−X2

Or

X1=[#C(Q,L,C)]/[#C(Q,L,C)+S21]

X2=(1−X1)·[#C(Q,L)]/[#C(Q,L)+S11]

X3=1−X1−X2

The first set of smoothing parameters can help in making the variousLC|C click fractions comparable across documents. This can reduce thechances of a first document returned for a query having an LC|C clickfraction that comes mainly from country/language specific data, while asecond document returned for the same query has an LC|C click fractionthat comes mainly from the base data.

Furthermore, it should be noted that different smoothing factors S0,S00, S1, S10, S11, S2, S20 and S21 can be used, or one or more of thesecan be the same smoothing factor. The smoothing factors used can bedetermined based on how much traffic is received within the context ofthe click fraction. For example, for a given country-language tuple, thesmoothing factor can be raised concordant with the amount of trafficreceived (e.g., a larger smoothing factor can be used for US-Englishqueries if a good deal more of such queries are received). In addition,the smoothing factor can be increased for query sources that havehistorically generated more spamming activity (e.g., queries fromRussia).

The measure of relevance can be output 4050 to a ranking engine forranking of search results, including the document result, for a newsearch corresponding to the search query. This measure of relevance(e.g., the LC|C click fraction described above) can be directly appliedto IR scores for the documents, or this measure can be passed through atransform to create a boosting factor that can be applied to the IRscores. For example, the LC|C click fraction can be transformed into aboosting factor for the IR score according to the following equation:IRBoost=1+M/(1+e^(X*(LC|C−0.5)))where M and X can be constants, such as:

(M, X)=(100, −100)

(M, X)=(50, −10)

(M, X)=(10, −5).

Employing a boosting function can help the LC|C click fractions todiscriminate among, and correlate well with, the relevance of variousdocuments.

Other transforms are also possible. For example, a linear form boostingfunction can be defined as:IRBoost=1+min(K,M*(max(0,LC|C−X)))where K, M and X can be constants, such as:

(K, M, X)=(infinity, 100, 0.5)

(K, M, X)=(49, 100, 0)

(K, M, X)=(9, 20, 0.1)

(K, M, X)=(infinity, 9, 0).

Alternatively, an exponential form can be used as follows:IRBoost=1+M·(max(X,LC|C−Y)^((N))where M, X, Y and N can be constants, such as:

(M, X, Y, N)=(90, 0.1, 0, 2.6)

(M, X, Y, N)=(15, 0, 0.2, 2)

(M, X, Y, N)=(5, 0, 0, 1.6)

(M, X, Y, N)=(0.5, 0, −0.2, 1.2)

(M, X, Y, N)=(90, 0, −0.2, 0.5).

Still further transforms are also possible. Such transforms can causelower LC|C click fractions to generate almost no boost (e.g., a boost ofabout 1), whereas higher LC|C click fractions can generate a significantboost.

In any event, the transform employed can be adjusted based on thespecific measure of relevance, and historical data combined with humangenerated relevance ratings (e.g., employed in a tuning process toselect an appropriate boosting transform for a given implementation).Moreover, the measure of relevance can be used to modify and improve theranking of search results generated for a given query, and the modifiedranking can be presented to a user (e.g., on a display device in a webbrowser user interface).

As mentioned above, the weighting used can also be adjusted based on oneor more viewing length differentiators. FIG. 4E shows an example processof such weighting. One or more viewing length differentiators (e.g.,query category and user type) can be identified 4310 for use in theweighting. A viewing length differentiator can include a factor governedby a determined category of the search query, a factor governed by adetermined type of a user generating the individual selections, or acombination of them. The document views can be weighted 4320 based onthe viewing length information in conjunction with the viewing lengthdifferentiator(s), such as the determined category of the search queryand the determined type of the user.

Thus, in the discontinuous weighting case (and the continuous weightingcase), the threshold(s) (or formula) for what constitutes a good clickcan be evaluated on query and user specific bases. For example, thequery categories can include “navigational” and “informational”, where anavigational query is one for which a specific target page or site islikely desired (e.g., a query such as “BMW”), and an informational queryis one for which many possible pages are equally useful (e.g., a querysuch as “George Washington's Birthday”). Note that such categories mayalso be broken down into sub-categories as well, such asinformational-quick and informational-slow: a person may only need asmall amount of time on a page to gather the information they seek whenthe query is “George Washington's Birthday”, but that same user may needa good deal more time to assess a result when the query is “Hilberttransform tutorial”.

The query categories can be identified by analyzing the IR scores or thehistorical implicit feedback provided by the click fractions. Forexample, significant skew in either of these (meaning only one or a fewdocuments are highly favored over others) can indicate a query isnavigational. In contrast, more dispersed click patterns for a query canindicate the query is informational. In general, a certain category ofquery can be identified (e.g., navigational), a set of such queries canbe located and pulled from the historical click data, and a regressionanalysis can be performed to identify one or more features that areindicative of that query type (e.g., mean staytime for navigationalqueries versus other query categories; the term “staytime” refers totime spent viewing a document result, also known as document dwelltime).

Traditional clustering techniques can also be used to identify the querycategories. This can involve using generalized clustering algorithms toanalyze historic queries based on features such as the broad nature ofthe query (e.g., informational or navigational), length of the query,and mean document staytime for the query. These types of features can bemeasured for historical queries, and the threshold(s) can be adjustedaccordingly. For example, K means clustering can be performed on theaverage duration times for the observed queries, and the threshold(s)can be adjusted based on the resulting clusters.

User types can also be determined by analyzing click patterns. Forexample, computer savvy users often click faster than less experiencedusers, and thus users can be assigned different weighting functionsbased on their click behavior. These different weighting functions caneven be fully user specific (a user group with one member). For example,the average click duration and/or click frequency for each individualuser can be determined, and the threshold(s) for each individual usercan be adjusted accordingly. Users can also be clustered into groups(e.g., using a K means clustering algorithm) based on various clickbehavior patterns.

Moreover, the weighting can be adjusted based on the determined type ofthe user both in terms of how click duration is translated into goodclicks versus not-so-good clicks, and in terms of how much weight togive to the good clicks from a particular user group versus another usergroup. Some user's implicit feedback may be more valuable than otherusers due to the details of a user's review process. For example, a userthat almost always clicks on the highest ranked result can have his goodclicks assigned lower weights than a user who more often clicks resultslower in the ranking first (since the second user is likely morediscriminating in his assessment of what constitutes a good result). Inaddition, a user can be classified based on his or her query stream.Users that issue many queries on (or related to) a given topic T (e.g.,queries related to law) can be presumed to have a high degree ofexpertise with respect to the given topic T, and their click data can beweighted accordingly for other queries by them on (or related to) thegiven topic T.

FIG. 5 is a schematic diagram of an example computer system 6050. Thesystem 6050 can be used for practicing operations described above. Thesystem 6050 can include a processor 6018, a memory 6016, a storagedevice 6052, and input/output devices 6054. Each of the components 6018,6016, 6052, and 6054 are interconnected using a system bus 6056. Theprocessor 6018 is capable of processing instructions within the system6050. These instructions can implement one or more aspects of thesystems, components and techniques described above. In someimplementations, the processor 6018 is a single-threaded processor. Inother implementations, the processor 6018 is a multi-threaded processor.The processor 6018 can include multiple processing cores and is capableof processing instructions stored in the memory 6016 or on the storagedevice 6052 to display graphical information for a user interface on theinput/output device 6054.

The memory 6016 is a computer readable medium such as volatile or nonvolatile that stores information within the system 6050. The memory 6016can store processes related to the functionality of the search engine1030, for example. The storage device 6052 is capable of providingpersistent storage for the system 6050. The storage device 6052 caninclude a floppy disk device, a hard disk device, an optical diskdevice, or a tape device, or other suitable persistent storage mediums.The storage device 6052 can store the various databases described above.The input/output device 6054 provides input/output operations for thesystem 6050. The input/output device 6054 can include a keyboard, apointing device, and a display unit for displaying graphical userinterfaces.

The computer system shown in FIG. 6 is but one example. In general,embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe subject matter described in this specification can be implemented asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.The computer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing apparatus” encompassesall apparatus, devices, and machines for processing data, including byway of example a programmable processor, a computer, or multipleprocessors or computers. The apparatus can include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them. A propagated signal is anartificially generated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal, that is generated to encodeinformation for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto optical disks; and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input.

Embodiments of the invention can be implemented in a computing systemthat includes a back-end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front-end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the invention have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. Moreover, the server environment,which is configured to provide electronic search service and employ theranking systems and techniques described, need not be implemented usingtraditional back-end or middleware components. The server environmentcan be implemented using a program installed on a personal computingapparatus and used for electronic search of local files, or the serverenvironment can be implemented using a search appliance (such as GOOGLE®in a Box, provided by Google Inc. of Mountain View, Calif.) installed inan enterprise network.

What is claimed is:
 1. A computer-implemented method comprising:determining a first number representing a count of longer views of adocument following selections of search results, where the searchresults reference the document in response to submissions of a searchquery to a search engine; determining a second number representing acount of shorter views of the document following the selections of thesearch results, where the search results reference the document inresponse to submissions of the search query; determining a measure ofrelevance for the document with respect to the search query based on aratio between the first number and the second number; and outputting themeasure of relevance to a ranking engine for ranking of search results,including new search results referencing the document, for a new searchresponsive to a new submission of the search query.
 2. The method ofclaim 1, wherein determining the measure of relevance comprises dividingthe first number by a count of total views of the document.
 3. Themethod of claim 1, further comprising: tracking individual selections ofthe search results referencing the document within the context of thesearch query for which the search results are returned; weightingdocument views resulting from the selections based on viewing lengthinformation to produce weighted views of the document result; andcombining the weighted views of the document result to determine thefirst number.
 4. The method of claim 3, wherein determining the measureof relevance comprises dividing the first number by the second number.5. The method of claim 3, wherein the weighting comprises applying acontinuous function to the document views resulting from the selections.6. The method of claim 3, wherein the weighting comprises applying adiscontinuous function to the document views resulting from theselections.
 7. The method of claim 6, wherein applying the discontinuousfunction comprises: classifying the individual selections of thedocument result into viewing time categories; and assigning weights tothe individual selections based on results of the classifying.
 8. Themethod of claim 3, wherein the weighting comprises weighting thedocument views based on the viewing length information in conjunctionwith a viewing length differentiator.
 9. The method of claim 8, whereinthe viewing length differentiator includes a factor governed by adetermined category of the search query, and the weighting comprisesweighting the document views based on the determined category of thesearch query.
 10. The method of claim 8, wherein the viewing lengthdifferentiator includes a factor governed by a determined type of a usergenerating the individual selections, and the weighting comprisesweighting the document views based on the determined type of the user.11. A system comprising: one or more computers; and a computer programproduct, encoded on a non-transitory computer-readable medium, operableto cause the one or more computers to perform operations comprising:determining a first number representing a count of longer views of adocument following selections of search results, where the searchresults reference the document in response to submissions of a searchquery to a search engine; determining a second number representing acount of shorter views of the document following the selections of thesearch results, where the search results reference the document inresponse to submissions of the search query; determining a measure ofrelevance for the document with respect to the search query based on aratio between the first number and the second number; and outputting themeasure of relevance to a ranking engine for ranking of search results,including new search results referencing the document, for a new searchresponsive to a new submission of the search query.
 12. The system ofclaim 11, wherein determining the measure of relevance comprisesdividing the first number by a count of total views of the document. 13.The system of claim 11, the operations further comprising: trackingindividual selections of the search results referencing the documentwithin the context of the search query for which the search results arereturned; weighting document views resulting from the selections basedon viewing length information to produce weighted views of the documentresult; and combining the weighted views of the document result todetermine the first number.
 14. The system of claim 13, whereindetermining the measure of relevance comprises dividing the first numberby the second number.
 15. The system of claim 13, wherein the weightingcomprises applying a continuous function to the document views resultingfrom the selections.
 16. The system of claim 13, wherein the weightingcomprises applying a discontinuous function to the document viewsresulting from the selections.
 17. The system of claim 16, whereinapplying the discontinuous function comprises: classifying theindividual selections of the document result into viewing timecategories; and assigning weights to the individual selections based onresults of the classifying.
 18. The system of claim 13, wherein theweighting comprises weighting the document views based on the viewinglength information in conjunction with a viewing length differentiator.19. The system of claim 18, wherein the viewing length differentiatorincludes a factor governed by a determined category of the search query,and the weighting comprises weighting the document views based on thedetermined category of the search query.
 20. The system of claim 18,wherein the viewing length differentiator includes a factor governed bya determined type of a user generating the individual selections, andthe weighting comprises weighting the document views based on thedetermined type of the user.